{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ed94040c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tushare as ts\n",
    "import pandas as pd\n",
    "from dateutil.parser import parse\n",
    "\n",
    "from scipy import stats\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.rcParams['axes.unicode_minus']=False # 负号问题\n",
    "pd.options.mode.chained_assignment = None\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c5336017",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_df(tscode,startdate = '20210101', enddate = '20220520'):\n",
    "    pro = ts.pro_api(\"0ba8feef618e5db7b1ebb65538fe51e4aef69fb3cbf709d44128f313\")\n",
    "    def init_df(df):\n",
    "        df['trade_date']= df['trade_date'].apply(parse)\n",
    "        df = df.set_index('trade_date')\n",
    "        return df['close']\n",
    "    \n",
    "    return init_df( pro.query(\"fut_daily\", ts_code=tscode, start_date=startdate, end_date=enddate))\n",
    "\n",
    "def get_dest_df(df1, df2):\n",
    "    # df_dest = pd.concat([df1, df2], axis=1)\n",
    "    df_dest = pd.merge(df1,df2, on='trade_date', how='left')\n",
    "    df_dest = df_dest.pct_change()\n",
    "    df_dest['x_return'] =(df_dest['close_x'] - df_dest['close_x'].shift(1)) / df_dest['close_x'].shift(1)\n",
    "    df_dest['y_return'] =(df_dest['close_y'] - df_dest['close_y'].shift(1)) / df_dest['close_y'].shift(1)\n",
    "\n",
    "    return df_dest\n",
    "\n",
    "def get_corr(df_all):\n",
    "    return df_all.x_return.corr(df_all.y_return)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "86d4da94",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9606552790997283\n"
     ]
    }
   ],
   "source": [
    "code1 = \"rb2209.SHF\"\n",
    "# code2=\"SM2209.ZCE\"\n",
    "code2=\"rb2205.SHF\"\n",
    "\n",
    "df1= get_df(code1)\n",
    "# print(df1.head(1))\n",
    "df2 = get_df(code2)\n",
    "\n",
    "if len(df1) == 0 or len(df1) == 0 :\n",
    "    print(\"没有获取到数据\")\n",
    "\n",
    "\n",
    "# df_dest = pd.concat([df1, df2], axis=1)\n",
    "# print(df_dest.head(1))\n",
    "\n",
    "df_all1 = get_dest_df(df1=df1, df2=df2)\n",
    "# print(df_all.head(1))\n",
    "\n",
    "print(get_corr(df_all=df_all))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "00b1d55f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ldq\\.conda\\envs\\trader\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n",
      "C:\\Users\\ldq\\.conda\\envs\\trader\\lib\\site-packages\\seaborn\\axisgrid.py:2182: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x146d643d900>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "sns.jointplot(df_dest['close_x'], df_dest['close_y'], kind='reg', size=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ba62e0cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ldq\\.conda\\envs\\trader\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Input \u001b[1;32mIn [18]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43msns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mviolinplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf_dest\u001b[49m\u001b[43m,\u001b[49m\u001b[43msize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m24\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\.conda\\envs\\trader\\lib\\site-packages\\seaborn\\_decorators.py:46\u001b[0m, in \u001b[0;36m_deprecate_positional_args.<locals>.inner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     36\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[0;32m     37\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPass the following variable\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m as \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124mkeyword arg\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m: \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m     38\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFrom version 0.12, the only valid positional argument \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     43\u001b[0m         \u001b[38;5;167;01mFutureWarning\u001b[39;00m\n\u001b[0;32m     44\u001b[0m     )\n\u001b[0;32m     45\u001b[0m kwargs\u001b[38;5;241m.\u001b[39mupdate({k: arg \u001b[38;5;28;01mfor\u001b[39;00m k, arg \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(sig\u001b[38;5;241m.\u001b[39mparameters, args)})\n\u001b[1;32m---> 46\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m f(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[1;32m~\\.conda\\envs\\trader\\lib\\site-packages\\seaborn\\categorical.py:2400\u001b[0m, in \u001b[0;36mviolinplot\u001b[1;34m(x, y, hue, data, order, hue_order, bw, cut, scale, scale_hue, gridsize, width, inner, split, dodge, orient, linewidth, color, palette, saturation, ax, **kwargs)\u001b[0m\n\u001b[0;32m   2388\u001b[0m \u001b[38;5;129m@_deprecate_positional_args\u001b[39m\n\u001b[0;32m   2389\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mviolinplot\u001b[39m(\n\u001b[0;32m   2390\u001b[0m     \u001b[38;5;241m*\u001b[39m,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   2397\u001b[0m     ax\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[0;32m   2398\u001b[0m ):\n\u001b[1;32m-> 2400\u001b[0m     plotter \u001b[38;5;241m=\u001b[39m \u001b[43m_ViolinPlotter\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43morder\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhue_order\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2401\u001b[0m \u001b[43m                             \u001b[49m\u001b[43mbw\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcut\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mscale\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mscale_hue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgridsize\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2402\u001b[0m \u001b[43m                             \u001b[49m\u001b[43mwidth\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minner\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msplit\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdodge\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43morient\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlinewidth\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2403\u001b[0m \u001b[43m                             \u001b[49m\u001b[43mcolor\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpalette\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msaturation\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   2405\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m ax \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m   2406\u001b[0m         ax \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39mgca()\n",
      "File \u001b[1;32m~\\.conda\\envs\\trader\\lib\\site-packages\\seaborn\\categorical.py:522\u001b[0m, in \u001b[0;36m_ViolinPlotter.__init__\u001b[1;34m(self, x, y, hue, data, order, hue_order, bw, cut, scale, scale_hue, gridsize, width, inner, split, dodge, orient, linewidth, color, palette, saturation)\u001b[0m\n\u001b[0;32m    517\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m, x, y, hue, data, order, hue_order,\n\u001b[0;32m    518\u001b[0m              bw, cut, scale, scale_hue, gridsize,\n\u001b[0;32m    519\u001b[0m              width, inner, split, dodge, orient, linewidth,\n\u001b[0;32m    520\u001b[0m              color, palette, saturation):\n\u001b[1;32m--> 522\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mestablish_variables\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43morient\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43morder\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhue_order\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    523\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mestablish_colors(color, palette, saturation)\n\u001b[0;32m    524\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mestimate_densities(bw, cut, scale, scale_hue, gridsize)\n",
      "File \u001b[1;32m~\\.conda\\envs\\trader\\lib\\site-packages\\seaborn\\categorical.py:156\u001b[0m, in \u001b[0;36m_CategoricalPlotter.establish_variables\u001b[1;34m(self, x, y, hue, data, orient, order, hue_order, units)\u001b[0m\n\u001b[0;32m    153\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(err)\n\u001b[0;32m    155\u001b[0m \u001b[38;5;66;03m# Figure out the plotting orientation\u001b[39;00m\n\u001b[1;32m--> 156\u001b[0m orient \u001b[38;5;241m=\u001b[39m \u001b[43minfer_orient\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    157\u001b[0m \u001b[43m    \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43morient\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrequire_numeric\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrequire_numeric\u001b[49m\n\u001b[0;32m    158\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    160\u001b[0m \u001b[38;5;66;03m# Option 2a:\u001b[39;00m\n\u001b[0;32m    161\u001b[0m \u001b[38;5;66;03m# We are plotting a single set of data\u001b[39;00m\n\u001b[0;32m    162\u001b[0m \u001b[38;5;66;03m# ------------------------------------\u001b[39;00m\n\u001b[0;32m    163\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m x \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mor\u001b[39;00m y \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    164\u001b[0m \n\u001b[0;32m    165\u001b[0m     \u001b[38;5;66;03m# Determine where the data are\u001b[39;00m\n",
      "File \u001b[1;32m~\\.conda\\envs\\trader\\lib\\site-packages\\seaborn\\_core.py:1311\u001b[0m, in \u001b[0;36minfer_orient\u001b[1;34m(x, y, orient, require_numeric)\u001b[0m\n\u001b[0;32m   1283\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minfer_orient\u001b[39m(x\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, y\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, orient\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, require_numeric\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[0;32m   1284\u001b[0m     \u001b[38;5;124;03m\"\"\"Determine how the plot should be oriented based on the data.\u001b[39;00m\n\u001b[0;32m   1285\u001b[0m \n\u001b[0;32m   1286\u001b[0m \u001b[38;5;124;03m    For historical reasons, the convention is to call a plot \"horizontally\"\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1308\u001b[0m \n\u001b[0;32m   1309\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m-> 1311\u001b[0m     x_type \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01mif\u001b[39;00m x \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[43mvariable_type\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1312\u001b[0m     y_type \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01mif\u001b[39;00m y \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m variable_type(y)\n\u001b[0;32m   1314\u001b[0m     nonnumeric_dv_error \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m orientation requires numeric `\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m` variable.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n",
      "File \u001b[1;32m~\\.conda\\envs\\trader\\lib\\site-packages\\seaborn\\_core.py:1229\u001b[0m, in \u001b[0;36mvariable_type\u001b[1;34m(vector, boolean_type)\u001b[0m\n\u001b[0;32m   1226\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcategorical\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1228\u001b[0m \u001b[38;5;66;03m# Special-case all-na data, which is always \"numeric\"\u001b[39;00m\n\u001b[1;32m-> 1229\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m pd\u001b[38;5;241m.\u001b[39misna(vector)\u001b[38;5;241m.\u001b[39mall():\n\u001b[0;32m   1230\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnumeric\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1232\u001b[0m \u001b[38;5;66;03m# Special-case binary/boolean data, allow caller to determine\u001b[39;00m\n\u001b[0;32m   1233\u001b[0m \u001b[38;5;66;03m# This triggers a numpy warning when vector has strings/objects\u001b[39;00m\n\u001b[0;32m   1234\u001b[0m \u001b[38;5;66;03m# https://github.com/numpy/numpy/issues/6784\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1238\u001b[0m \u001b[38;5;66;03m# https://github.com/numpy/numpy/issues/13548\u001b[39;00m\n\u001b[0;32m   1239\u001b[0m \u001b[38;5;66;03m# This is considered a bug by numpy and will likely go away.\u001b[39;00m\n",
      "File \u001b[1;32m~\\.conda\\envs\\trader\\lib\\site-packages\\pandas\\core\\generic.py:1537\u001b[0m, in \u001b[0;36mNDFrame.__nonzero__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1535\u001b[0m \u001b[38;5;129m@final\u001b[39m\n\u001b[0;32m   1536\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__nonzero__\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m-> 1537\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m   1538\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe truth value of a \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28mself\u001b[39m)\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m is ambiguous. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1539\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUse a.empty, a.bool(), a.item(), a.any() or a.all().\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1540\u001b[0m     )\n",
      "\u001b[1;31mValueError\u001b[0m: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all()."
     ]
    }
   ],
   "source": [
    "sns.violinplot(df_dest,size=24)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "51de9160",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ldq\\.conda\\envs\\trader\\lib\\site-packages\\seaborn\\axisgrid.py:2076: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x146dae14a00>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 345.6x345.6 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(df_dest, diag_kind='kde', size=2.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3c5707ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>close_x</th>\n",
       "      <th>close_y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trade_date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-05-12</th>\n",
       "      <td>-0.011310</td>\n",
       "      <td>-0.003976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-11</th>\n",
       "      <td>0.019426</td>\n",
       "      <td>0.008583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-10</th>\n",
       "      <td>-0.020114</td>\n",
       "      <td>0.008510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-09</th>\n",
       "      <td>0.012532</td>\n",
       "      <td>0.002943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-06</th>\n",
       "      <td>0.016219</td>\n",
       "      <td>-0.005674</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             close_x   close_y\n",
       "trade_date                    \n",
       "2022-05-12 -0.011310 -0.003976\n",
       "2022-05-11  0.019426  0.008583\n",
       "2022-05-10 -0.020114  0.008510\n",
       "2022-05-09  0.012532  0.002943\n",
       "2022-05-06  0.016219 -0.005674"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df_dest = pd.merge(df1,df2, on='trade_date', how='left')\n",
    "df_dest = df_dest.dropna()\n",
    "df_dest.head()\n",
    "#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "78a2ba99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df_dest.corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "714527d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.matrix.ClusterGrid at 0x146d651a230>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.clustermap(df_dest.corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1c0c6855",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>close_x</th>\n",
       "      <th>close_y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trade_date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-05-13</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-12</th>\n",
       "      <td>-0.011310</td>\n",
       "      <td>-0.003976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-11</th>\n",
       "      <td>0.019426</td>\n",
       "      <td>0.008583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-10</th>\n",
       "      <td>-0.020114</td>\n",
       "      <td>0.008510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-09</th>\n",
       "      <td>0.012532</td>\n",
       "      <td>0.002943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-24</th>\n",
       "      <td>-0.010043</td>\n",
       "      <td>-0.013685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-23</th>\n",
       "      <td>0.004575</td>\n",
       "      <td>0.019347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-22</th>\n",
       "      <td>-0.011089</td>\n",
       "      <td>-0.008819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-17</th>\n",
       "      <td>-0.005206</td>\n",
       "      <td>-0.020503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-16</th>\n",
       "      <td>0.026570</td>\n",
       "      <td>0.025276</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>154 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             close_x   close_y\n",
       "trade_date                    \n",
       "2022-05-13       NaN       NaN\n",
       "2022-05-12 -0.011310 -0.003976\n",
       "2022-05-11  0.019426  0.008583\n",
       "2022-05-10 -0.020114  0.008510\n",
       "2022-05-09  0.012532  0.002943\n",
       "...              ...       ...\n",
       "2021-09-24 -0.010043 -0.013685\n",
       "2021-09-23  0.004575  0.019347\n",
       "2021-09-22 -0.011089 -0.008819\n",
       "2021-09-17 -0.005206 -0.020503\n",
       "2021-09-16  0.026570  0.025276\n",
       "\n",
       "[154 rows x 2 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dest = df_dest.pct_change(1)\n",
    "df_dest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8e836b68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>close_x</th>\n",
       "      <th>close_y</th>\n",
       "      <th>x_return</th>\n",
       "      <th>y_return</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trade_date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-05-13</th>\n",
       "      <td>4686.0</td>\n",
       "      <td>5030.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-12</th>\n",
       "      <td>4633.0</td>\n",
       "      <td>5010.0</td>\n",
       "      <td>-0.011310</td>\n",
       "      <td>-0.003976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-11</th>\n",
       "      <td>4723.0</td>\n",
       "      <td>5053.0</td>\n",
       "      <td>0.019426</td>\n",
       "      <td>0.008583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-10</th>\n",
       "      <td>4628.0</td>\n",
       "      <td>5096.0</td>\n",
       "      <td>-0.020114</td>\n",
       "      <td>0.008510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-09</th>\n",
       "      <td>4686.0</td>\n",
       "      <td>5111.0</td>\n",
       "      <td>0.012532</td>\n",
       "      <td>0.002943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-24</th>\n",
       "      <td>5027.0</td>\n",
       "      <td>5117.0</td>\n",
       "      <td>-0.010043</td>\n",
       "      <td>-0.013685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-23</th>\n",
       "      <td>5050.0</td>\n",
       "      <td>5216.0</td>\n",
       "      <td>0.004575</td>\n",
       "      <td>0.019347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-22</th>\n",
       "      <td>4994.0</td>\n",
       "      <td>5170.0</td>\n",
       "      <td>-0.011089</td>\n",
       "      <td>-0.008819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-17</th>\n",
       "      <td>4968.0</td>\n",
       "      <td>5064.0</td>\n",
       "      <td>-0.005206</td>\n",
       "      <td>-0.020503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-16</th>\n",
       "      <td>5100.0</td>\n",
       "      <td>5192.0</td>\n",
       "      <td>0.026570</td>\n",
       "      <td>0.025276</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>154 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            close_x  close_y  x_return  y_return\n",
       "trade_date                                      \n",
       "2022-05-13   4686.0   5030.0       NaN       NaN\n",
       "2022-05-12   4633.0   5010.0 -0.011310 -0.003976\n",
       "2022-05-11   4723.0   5053.0  0.019426  0.008583\n",
       "2022-05-10   4628.0   5096.0 -0.020114  0.008510\n",
       "2022-05-09   4686.0   5111.0  0.012532  0.002943\n",
       "...             ...      ...       ...       ...\n",
       "2021-09-24   5027.0   5117.0 -0.010043 -0.013685\n",
       "2021-09-23   5050.0   5216.0  0.004575  0.019347\n",
       "2021-09-22   4994.0   5170.0 -0.011089 -0.008819\n",
       "2021-09-17   4968.0   5064.0 -0.005206 -0.020503\n",
       "2021-09-16   5100.0   5192.0  0.026570  0.025276\n",
       "\n",
       "[154 rows x 4 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "cc44036b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'close_x'}>,\n",
       "        <AxesSubplot:title={'center':'close_y'}>],\n",
       "       [<AxesSubplot:title={'center':'x_return'}>,\n",
       "        <AxesSubplot:title={'center':'y_return'}>]], dtype=object)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_all.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "8e588fdc",
   "metadata": {},
   "outputs": [],
   "source": [
    "xret = df_all['x_return']\n",
    "\n",
    "sigma = xret.std()\n",
    "mu = xret.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "5871a17c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.arange(-0.06, 0.062, 0.002), stats.norm.pdf(np.arange(-0.06, 0.062, 0.002), mu, sigma))\n",
    "xret.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "11a01316",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.02020726770441512"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sigma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "3272ab92",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.002404395430865193, 0.004072375558926422)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  进行区间估计, 置信度为0.95时的置信区间\n",
    "xret=xret.dropna()\n",
    "stats.t.interval(0.95, len(xret) -1, mu, stats.sem(xret))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "4539f7e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='trade_date'>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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bcZAyCflsCqKDTa26DctxGs6n4HOlXaE0X2NPvz6RC3TH6lme0l+Ipx9S+q22Yjg4IYM+APSkTR30F4uVpvTTDTx91fKp1BwIgYig787I9eyd4OuwypouWyjXbNhCwCDCpv4cjkYkuydL1UQtGjgR3U11+hZX72Tk3VA32ArdgG0ntHfaPGfKkfZOlyl9d4yLae+08rlmyjVMzFWxdagAALLCrAPfy6oO+u0ewMuFV6fvevqRJZvqLbKrVML2Dnv6fsO1aKUPyFm4jpAXik39PZH2znU33Y///Y17Eo+/m+wdRwiYJEs2gbXVU39iror/c+O9ePxEqe6xmuM0XFHN8u7a2ltn2SvZjFH63dAHiStl1Lu/o1PzOO8DP8IjT8wkeg0+nta34elz5Q4r/XzGXJDV1C6rMuhnO5zIFULgB/cfazrblU+KtOvpN5qcBfgHXJ3SN+R7eitnOWFP3/Ysr9GZCoSi9GcqFmZCjaOOTZVxotS8L49fvdM9F1fL5pJNGfS7pUZ8Obj74An80389jrf83V11+9SyRcPjUT1X2jlvoiZn2Y5AL198u2A/eJ6+IgSOTM5jar6G/eP1F8oo2L7y7J0WPH3urrl1iIN+qiPfS9tBn4huIKLbiei6pNsQUT8R/RsR/ZiIvk1EmXbfvxEZUyrhTtk7dx+axP/46h78bO9Yw+349jdlEtJxbRgCyTB3qcSQ0jeJYAdKNuurd7a66uL4TAW24wb9gR4A9RU8sxUrkXrv1slZKZNQyMrvqFtmgy4HfIF76IkZvP0f7vaOAyEELEc0tOFUVd7OeRPdhkGgP58OjK2TRFXv8LGbNP/Hzx3IZ7xZuUmJUvorZnIWEb0CgCmEuAzAZiI6NeE2rwPwCSHEswEcA/C89oceT6c9/Ql3cZJmDc18e8eIbcOgqva5OKVfZ+/Ue/rb10kfcXRG2jumIZU+EB30nQRVL92o9DlfwfZONwSb5YI/6zueeSpueWgUH/rebwAo7bkbBDb1mGnHFo1S+jXHwYAb9LvK01esprgy5zg46PekTTkXpIXPdXCihKFCBr3uIkaFbGpRGsC1SrtK/0oAN7q/3wzg8iTbCCE+I4T4sfu3EQDH23z/hvhBvzMqb9q9tT7RZHKYn8iVnn6UEgus+cpKP6a1Mh/A4dvzmYqF7a66GJutwHEEiOAH/VAyd65iJWrEVvNUUpcpfbefPrC0qzZZtoO/+9ljbZc5LjZcPvjWK3biLZfvwN/dvh9/f/v+ulYeUajHTKu1+jXbCbQJYSxboL/HDfpdcMfFVorqo9diih/ikG1QpEgr5lItlWwenJjDya61AwA9GTO2qunQRGnJFidqN+gXABx2f58GsKGVbYjoUgCDQog7w08iomuJ6C4iumt0dLStwXV6cta0u7OmmvjiluN7+ikz2tNXD0Y+WCMXUVGeq6q2mu2gajno70kjZRCqliOTnQZhQ18ORKgr25yr2EgQ8xfUWtlxBHZ/6N/xlTsPtPzcRrCnz/bOUpYK3nNoEu//7gO47eHGNt5yMVexQCRtg/e84Ew844z1+NN/fQCTrvhopGZrlurptxag1cSoepdQsx3kMymkDOq40hdCeOeP2vqARVUrSr/HrQwrZFItlWweGC9hmxL0Cw1ahTzzE7fis7fsS/zardBu0J8F0OP+Xox5nchtiGgIwKcAvDnqhYUQ1wshdgshdo+MjLQ1OE5adsremXav/s2UftXyPf1UTO8d9ULASiWq4Zqq4oIzIeVzCtkUDEN6/7aQz0mbBkaK2YDSr1g2qraTyLLxJme10YahbNkYm63gvTfd3/JzG8EXNFb6SxlsWL0upH/RYjJTsVDMpEAku7Y++6wNsByByXkpPqJyLzc/+ASe+pGbAzZYuUWlX1ZUvKr0bUcgbVLHShNVym65MxDt6ScVLqWqjbybU+ttQelXLQdHJuc9Px9wE7kxokQuo0qJXrtV2g36e+BbOucB2J9kGzdxeyOAdwshFlfiKfCX1bGgP8/2TnKlb7oq/C9+9FCgUZx6MHKQCSt9ouB26t0BVxcUcymY7h2BI6S9AwCbBnpwTOm/w8niJDY930m1Y+/ULP85izlrllsr55chkcuKcaHLTi4WcxULxVzK+3/aFT/sG0ddyO97fAqHJ+cD+adWPf04pc8L2hQ6lLBUUe/41GPCt0STHYNzFcvLF7XS3+nw5Dwc4VfuAG7JZtWuO/55nQ3ef4tNu696E4A3ENEnALwawK+J6ENNtvkegLcAuBDAHxPRLUT0mjbfvyFEhEzKQKVT9o7r6Tfz5Lw2DAYhZRpwBPCpm/fiO/cc9rYJ2DtJlX7ETMhiNuUmi90ZuW7U39yfwxHlIsOKLJmn3769o9aMP35i8bqh2o6AYRCyKbPl6opW4WA3NruwZScXi1klIAG++OGgH3Vx5iUz1dLDVsWSGvTDM3LTRncofbZRZOsDfyxWi4nciVIVgwVZdFjMpRMr/QPjsq31NregApB331HN6NRS7qWgrVcVQkxDJmrvBHCVEOJeIcR1TbaZEkJ8VggxKIS40v33jYUNP56saXRQ6bO90zgYqDs3Zfi3cvuO+33P1eAbp/QNI3jrHjUTspBN+S2Y3ZJNANjYn8PRqbKnNvjkT2LZeIncNuwd9ST7r4MnWn5+HJzIBZa+DprbCXeNvVO2UMxGKH03EEddnMdn64N+q4lptQKlrCp9m8tnUx2vomLxs66QjVT6SYXLibkqhvJu0M+aiT19rtEP2jtSvIUreKpeXOguewdCiBNCiBuFEMcWss1SkUl1Luizwj8x10Tpe4uoSHuHeXRs1vu95jheYpoVSiaiZFO9OKgB1bN3PKUvG7MZBiv9HpSqtpeH4JOjWcmmrS7P2I7SV+ydu/YvLOjvPT7rKUxurQxgydfJLXWhvdOr2Dt88eOgH3Vx5pXT1KDcsr3jfscGBWfkWo6DlGmgkO1MjxkV3lfDvZlAkPV6DiW0d06oSj+b3NM/MF5CLm14M3kBYIPbk/8/fhMsYlTbsywFq3JGLtDZoJ/c3uF++hS4qj86OhfojZ/1gn5cySYF7JKoCV1e0Hd79Biep8+1+tJimU1o76gXlnYSuep4Hzw23fLzmfmqjRf89W341p7H3bE4nnWVzyztOrnlbrR3MorSDx03UfuU7Z3ZRbB3+nvSAaVfs4W0d7pg6Uo+D4aLWWmpuONsRbg4jsCJUg1DBVmGWsymMV+zE1mhB8ZL2DqUB5F/nj/37I3YvW0Q7/+XX+PQhD8jWF1GdSlY3UG/w57+bMVqeAKxukgbsg0DMzVfw7h7Mso2yDLIc5AJK32T6ss0Gd/eMb3STkf49o5fqy+TuXzyO00O5GogcdyG0g+MsX0VODVfQ9VyvOBlO4Dpniz5JVb6vqffHUp/thxM5HIVm3oXFE4aekF/IUrfff3BfCag9OXsaEMq4k57+soyh+r/W/H0Z8py/sog2zu55BMAD07MeY3WGNMg/OVrzocA8Ac33lM3UUwr/RbJdNjT51vrRmpfVfrso+4akQfGo6PS16/Zjqf05z2lX2/vqGpbvY3nA1K1d2zF3tnUH2zFwBeJZuq9ZkXfWSSF7Z2etLmg9sfcY8YPbI733RezS6v0WUFPlmodaeNdsWxc++W7vGZhs5Wgp+/ZOwE7w99XjiP8RG5l4Z7+QD5dV6efMgj5rrB3XKXfmw383w+0zY9htsLWFX1PH2ge9IUQgZbKKicP5fHBl56NX+4/gc/dui8wlowO+q3RKXvHcQRmyjWcNCiD6WSDZK7ae2e/u2j1s86Sc9j2jUpf33aEt1IWB8fwwSA9fTUIq90N1USu36OH7Z31vVkYFGHvNPH0a4HEcfvVO/096QV1wuRgxbXllpKkzi+xraBWqnDwXE6OTpbxoweewC/3n4AQoi7os70zH/LZmcn5mjcJb1Zp0NbqecPfw0A+U7dcYvckcuW41hV4xSouY02u9Lkww1P6WbevUBNff3SmgnLNiQz6APDyC7bghU/ahL/88cO47/FJbe+0S6fsnbmqJetx3dKsRhO0vNs4w/AU94uftBnZlIFH3aAvlb5fc54yyFPpDNXZO8HqnWzKQNo03G6cbrLTDYwp08D63hyOsL1TSWjvhLzbVuE7BRn021eBfMKVLZ5fIBSln1riOn3/tXmBmuWEP3PNdlCuOXAEgnX6RvAOUW7r76uJOX/Mi2HvDOTTXtAXbpVYyjBQzKRQtZyOLmrEVVzcHdObu9DCkp8TbmHGUKE1e+eA69erLRhUiAh/9rJzMdKbxbu+dZ+2d9olYxod6afPVTBbh+qVPve9YbhlgGEQ3nbVKfjeOy7HOVv6sWO4gH2uvWM5fiK3XLPr/Hyg8YxcVf2ZxNU7vhoGZDL32LRU+kntHfWC2lb1jht8+noW1nSKq5M42FiO8D39JU7kqheU8Q4ofe6RU7UczFRkQArU6afk9xCcgervq3ElAa2q1VZ773Dp6kBPxjvnal4FilzFDFjaPkjN8JV+nKffXLicmAsr/YRBn7trxgR9AOjPp/GyC7Zg7/HZwHe3FKzeoN8he4dn425zkzbc92RqvobLP3ozvnvfEW9b9jwBOd6zN/cDAHaNFD2lrwb9UrV+qUSA6/SjPfZZZZYmt2GQJZv+8zf39/iJXOXEbKT2VWXUVp2+Yu+o1RStMusGu4pSjcF3MYUWZky2w3zNxqDbRXKsE0rfvdBVbccL2r0BTz/K3vH3lXqhYpssZVBbvXcyKQP5jIlyzfZUPgA3ket63x1c0KZUsZDPmHUtt1vpsjnhCrihQijoN7F3Do7PwSDgpMH4oA/IXJ16Lmil3yLZDgd9vpVjH/D4dBnlmoOHlRV6araI3LE7Rwo4dGIeFcuGZTte//z5hEo/XL3DZXxeGwYnqPQ39udwZGre84WZRr5+baFK3903fW4XxnbVPit9rhqxhW/vFDIplGuN14ZdCOWa7e3nTlTwcHlk1XICpblMxqwP+up+U4P+bMVCxjSQTRktK/1yzUZP2kQuLWeVW47wLuopw29z3amyTSEExueqyGdS3uI67Xj6E3NVZN2LG6DaO41Lsw9MlLCpvyfy3FXhWMDfkw76LdIpT5/tnc0DOaQM8jx9/smKGuDJK/W3cLtGirAdgQPjJTjCr9YpV6ODPjWYnBWwd4wYe6c/h3LNwdR8LXBiNqo/rsXcWSTFs3fc3uKlWnsBwbd3HDhuzxIuf/VU3RItSVeq2hgpZpFNGbH2jhACP9s7tqj9hRi+0NXsxvZOOWDvKJ6+Yu/IXi9u+5I2JmfJoO+XFqstRpaj+V0cB8dLeOFf/ye+ffdhbB7IeQGb7R0ugEhyDE/MVTFUyHi19r690/j4OjAeXbkThs9zvjvV9k6LdKpkk5V+f08a/T1pr6SQqzvUBUtqtvBuwVV2umWbDx2TdwWevVOzI8u4DIqfLDVXsb3gJxdbCZZsAsBmdwWtI5PloNJvEPQDpXlt2DuWYu8AyRqjHZsq44IP/gh7Dkx4f5ut+J4+f27+igpL7CXP12Sb3eFiNtbe+eX+E3jdF3+O2x5Z/PbLqtL37J1cM3vH328TcxX05VJecEmZBrIps63JWT0ZU8k9OUo5sqEo/eX19O98dBwv/fR/4vDkPD7yinPx1Wsu9toiz9eCSj+JQDwxV/X8fKAFeyemXDOMN/Pe6xOklX5LdMzTd4N8Xy4tF1lwgxIndLk0EpC2SCbiar5zpAgAnhXE1Tu2I5AJzcYFpG0TtHfCnr4MrKz01ZJNQNo7PDb1AG5s78jHDGrP3qlawaCfxN657/FJnCjV8N17j3p/4/FW3HUCAF/ps6rjffCrx6fwwJH2Z/+GKbsKd7iYie2/85jbUuOR47ORjy/o/RWlrzbWY1gglGKqd8bmqhguZpFzj6m0aSCbNlqv06/ZyKVNz4asWLYnBNIm1e2H5eAbvzyI13/x5xgsZHDT256Kqy/air5c2hsLX4BaWS5xolT1/HxAnk89abOhvTNTrmFirlo3MSsK3l+8L6ME4WKwuoN+J+ydeV9xqb05PHtHaW5muTMWwxSzKWzoy/pKX2mwFmfvxHXZlPaOq/QNgh0q2QRkIpfHpiY+GyZy3aDdkzYXZu+0oPS5CuKnD/uL67CtoSp91dOXry0/03XfuR8f/v4DicY3NlvBiz51Gw42WDC7pCj98ZhWDNxBlOdhLCZ8t6UmclV7h63DcI97ZmJWBjE+vjImtVX1Jj19I1rpG4Z3IVrIfIyk2I7An/7rA/jDf/oVLt21Dt/+vadix7AfcHMpE0R+W2y/DUOy6p3BQnBZb1XYRRFeF7cRfG6zDZZJaXunJTJm67epi8F0uYZCxvSmn7PnzEq/YjleRU/Vjvb0AWDncFFR+v5uysbYO5ZX20sId9n0E7lw2zAg0ANkpDcL0yAcnZoP+K6NyjZZGeWzqUAfnaSE7Z0kVTaPue1pHx2b84LxjFKnb/Pdh+FX7wC+wjw+XW7aBI954Mg07j88jbsPxTeDm3dXUVpXzMQmcrmnymNLEfS5escSnq/cG9VPPyaRyx4130mmU1Lpt2zvuN9DwNNXJh4uVyJ3plzDNX//S9zwn4/hTZdtx9++6Sne8cUYrjr3E7mN6/QfOjaDL972KAD5fXG1FtOrnONRcHfNrQ3KNRneX7Pa3mmPTpZssnrtzaW9Uji1zfIR1+KxbMebQBNm1/qCN6lDbbAWV73D8TljGt4BbDsCpartVRmo9o56PJkGYUNvFkcny5gpW17ZXyOlz3dR+Ux7Sr8de+fA+ByG3d4ptz4i1b7v6TueHeUpfU7kVmQZ4fhsNfG6o7y/4iZdOY5AxXJceyeL8blq5PfFSn8pgr5q78xWakgZFBAI4X76QH3J5rqir/RT7joE7UzOUhO5FctRErmGtx+aJTwXwom5Kl7xmdtx2yNj+PDLz8H7X3J25F004C5eUguXbEYfw5/+yV586Hu/ge0IzNfswJ0UIJV+o4tZO0qf74jixr9QVnfQt50lqZpoxHS55lWk9OZSnt93olTzVqs65iZzud94FDuHi97ybuqJHBn0lT9lFbsl7PNyGwa1nz6zaaAHew6eQMVyvMRuI0+/qtg77TVc8ydnAcnsnf1jJTzt1GGcPNTjWTzq5CxWbdxamcvz5qoWpssWqrbjJdqbwXZNXAdNVs8c9G1HRF5QDp2QJ/2RqfmAzbIYVJRELq/dqt7BEcllOKNaCcuOkVWsK2R9pW8arr3TpqfvHpuVmh1oJdCTNmFQtL2zb3QWL/v0z3B8ulz3WCv89JFRPHJ8Fn/z2ifjdRdva7htT8ZUZuTGe/q2I/DTR/g4q6FmC2+pRKaQaWzvHJwoYaiQQW8uHbsN49s7/oIvS8GqDfp8ADby9ZO0RG2V6XnLC2Sqpz9ZqnreIi9EXnOi6/QBYNf6ovd7wNOP2F490bMpX+mrC6gAMhgKUV+yCciyTVYlTz9drk3cuGRTPpbPmG21VmY7ylf6jW/9yzUbR6bmsX1dAVecOoLb944FqlYqluONV+2nD8iTiHvez1SsRPudlX6cbeMFfdfeidq2XLPxxHQFu0YKEMK/1V8sVKVfrjme0lZJm0agZJWD3NR8DbYjMFTIeIvyZBZi76glm5Zv76RNAhHFBsf7Hp/EPYcmceNdh+oeOzw57xVGNIO/i3NP6m+6rdrqudHkrHsfn/SsWK6+4+ofpphrZu/MxbZfCJM1g0o/zgVYKKs26DdbHP2X+ydw9vt+sOg9U1Slz0keIWQ3w1PXF2EahGOqvROr9P3kUxJ7R308LugbVL+ICsMtljf25XDGxl4Ayer085lUm20Y5HNYAYWVvhAiUOl0aKIEIYDtw3k8/bQRzFVt7DlwwiuJrSqWgqf0s35NtlpHn0Tt8/axQb/qK31u1xuu4OFlKJ92qryILrbFw03mqraDihU9WztlBudw8N0Qf751xYz3PF/pt1eyyReP6XkrkMgF5L6IskE4YN541+N19tjrv/hz/NWPH0k0Bh5z1HcQpidj1i0sE2Xv3PKQXzDAQT98Ye1t0kzuwHipYfsFFT63+fXSCT5LO6zeoJ9qHPT3HZ9FueYEFiFfDKbLvqdfzKZQs4WXvB0uZj3vHHDtnZir+ZaBHu8AbmrvKPE7YxqeypoJTc3n6h0nVLIJ+C2Wrzht2AuajYI+f6/5jBnrh47PVmIvqlVbIGMa6ElHL2D+s73juOwjN3sJWw6Y29cVcNkpw0gZhJ88dBxzSmsKv9QtWL0zW7ECdfRJfP2J2eRKn9v1hit4Drl+/hWnDQc+w2LBNkzVclCpOZEBL3xnyPuKg5iayE0ZhGy6DU/ftZa2rytgU38O1//0Ue/4YPuyELO2AR+jBydK+MX+icBjozOVyFLYvcdn8dlb9gWsW1b6UXc7YXhBcsCfnBWl9G99yF/Rii+S+QilH+fpVy0HRybnE/n5QNSMXG3vtESmib3DB9tMwtvHpEzPW+hzE6f8c7pcw+R8DYP5DDYN9HgTtBpV7xgGeXZQ86Cv2Dtpw/f0XW/Qs3fc6p1wySYgZxADUpXy6zVaMjGQyI2p3nnPt3+FP7jxnsjH+C7HdJOP4Z76e4/PQAjgcdcTV9cYLWZT2L19EN//lazX5+Quf16+aJkGIZc2UKraGFOVfoJ9zn1W4koxVaXP7XrDFwge+5mb+rCukPHKNiuWjd++/k784rFgkGuVsNKPs3dULC/ou73hC9mgvdNiAYSa0M6lTVz3wrPwwNFpfO3nBwPvX4xZHH2mLNee6M2m8M27Hvf+LoTAXNWKtP3+8RcH8dEfPOhZL4DfJC6R0k/73VfjPP3x2QruOzyFC7YOAFDsnbCnn/Xv5sMcnpyXHXdbVPo8Nm3vtEgze4eraqL8uD/91wfwo1+3vqwv99L3lL4b9I9OlmE7AgP5NDb253DMTVpZjtOwLGuXO0krqxxo0Z6+/3s2ZXollH4vffl8v3onmAcAgKefth7vfv4ZeM7ZGzylnKRksyeTiq3eGZ+txir9mu141QlSeQX3w3H3eTy/YXpeJsLZOnv6aeu9yhh/YQzL+5wM+7ctK/05P+hHndCq0h/MZ2AaVBf0D03MI20SNvTmsGO4gEfdoH/4xDzueHQcn/jxQ03H0Yiwpx9n76jwBZoT1NLeURK5qdYSudzemYPhC87diJOHenCrm2j3k+px9o48X1503mZ8/1dHlUVxHAgRneDniW7q7PayZcM0KFGZYz5jNq3Tv+2RMQgBvOCcTQAUeyes9JW7+TAH3BLjbeuaT8wClERu1fK67y4FqzfoN7F3ZmOUvuMIfPmO/bjpnsMtvyf30vc8fXeRBa7gGMxnsLk/hyOTsrmZtHfidyyvohWo0484sc1QIpd75nPQ73XHwW0YnFDJJiCD11ufvgvZlOkdbM08/ZRByJgUW71TsZzY+vuq0mwun6nve//ENAd9ebKV3GQhj+3pp4142464iVQOKup3WnAV5vhca0H/xFwVRHArfuo/g6r0DYMwVMhE2DslbBnogWEQtg8XPKXPn+nORyfw6yNTTccSh1q9E6f0m9k7g3k/kZt2K21aaX7nfQ9uMCQirO/N+b60oSp9/3VLVbmU6ExZLub+qt0nYb5m43v3ybs3Pm6igv5eL+j71mzcRS+KQtb0rKa4Ngy3PHQc6woZPGXHEAD/ji9cvdMb6ql/+74xrzpPvTtNgjeDumIvmbUDLCDoE9ENRHQ7EV3XyjZJnrcYcNCP8ye5lDKs9KfmZWkWL1fYCtxsTa3eAaTiA4DBQhob+3s8j79mOw2TNdyOoRV7hx+v2U5gfVzAV/pRJZsqfBFxnPjAzx1CU0oOIUzVclCq2BibreCc9/0QdymerZrEVkvomOMz8sThSW1zVdsrwQSAMzf1egti8E8OKupny2fkCT42U/VO0GZBn8sZ+bY8yldWlT4gV2Sqt3fmvcqNHcMFHJ+pYK5iBSaIffXOgw3H0ghf6UulmUjpu8FtYq6KvlzKtXRcT9/tkzNXtZsuosPMR3jpan+aoKfvn2uv/vwd+IsfP4SZcg29uRQuOHkAp6wv4pvuAvfcfyZ8B1iqWl4eTlX6cRe9KHrS/hoOfOejHsOOI/DTR8ZwxWkjKLj7l+2wuuodZeKZEAJv/rtfepO5DoyXkEsbWO8en81QE7lLZe0AbQZ9InoFAFMIcRmAzUR0apJtkjxvsWjm6fOVOVzJwSf4/vG5xAc+w6+l1ukDvtIfcJU+IOu2LUcg3UDpX7prHS7aMYSzNvX5nytqRq7yJ35cTtgJVe8o/fTD9o4KL0IyNlfBeR/4kae+VKqWDNopk2LtnYplY65q4fCJecxWLNxzaNJ7rGY7itKvX0P1+HTQ3pmvWoEkGhF5at/z9L1JLf5nKypKn+2yZkF/yl1G8NT1soopKpmrKn1AXnhGQ0r/8YmSt2wm52ceG5vz8gWD+bRXydUOasO1cs0OVHkxdZ6+e0zLiVnye+OLRUbpfZ+0M2m5FvweAARmrfKFPbx05ZHJMvYdn8VsxUJvNg0iwqt3n4Q9B05g7/HZWKWvirGw0s8lVPpsJ/LdNgBPDAHAfYenMDFXxZWnj3hBfsI9DsOJXD63ZsoWJuaqKNccb/8eGC9h61C+4bmmwuduxWosBhdKu698JYAb3d9vBnB5wm2aPo+IriWiu4jortHR0fDDieGa11qcp++q8umQ0mcPulxz8MRMaxNGvKDfEwz6XIEymM94zc2OTZXdyVnxu2BDXw43vvVSrO/Leb59XO8dhv1/y5b2Ttr0Z2l6/fQj7B0VVvqj0xXMVix89AcP1lk4VdtBJmUibRixidyK5XgtmwF/dirAdwqu0o+wFPi7V+2d8An3/HM2wjTI80z5ImcqV8G8q1zHZ6vYMtiDjGk0DfpcqXHaBnmRiAr6pZDSl/13/O24TJQXztjujnH/+Jx397KhL7eg/lBeGwbbQcVyPJtGhYM+HyKs9MdnK17zMFbIaaVlQrPOkQyvmqUGfbUpWcqzd8yAvVO1HIzPVTFT9hf5edkFW2AahG/uOeQp/PBx8chx2ZrENCjQprxiOYHcVyN6MiYcEZzbAfh5qlseOg4iWdTAn4uVflTJJiCPPb7z4DhwcGIuUaM1Rj23G9m+C6XdoF8AwKb3NIANCbdp+jwhxPVCiN1CiN0jIyPhhxOTvHonOugDwGMtWjx8AelXSjYB4P4jUzBIVshsUpqbVRvU6YdRV9gKox4fuZC9U8imvIuC6Sn9JvaO+4Kc0Ds4UcI/7Xk8sE3Nkh1CU6ZsARF1V8T5FE7KBoN+SOkr/fTLNdurzOCfUUH/mWduwJ7rnoUdwzKwsiWg5jgKGROlioXR2QpGiln09aQjPXqVCS/ou0o/IhldDil9tnc46cuflZX+dneMj43OYWKuhrRJGMxnWl6wRKWSSOn7F1bAV/rcdwfwlT73iwKSd8QM21wAAk3JVHtnvmZ7QbZqOZhwgz6Lo/W9OVx1+nr8838d9lo2hJX+3uOzMA3COZv7goncWvQ8hSjYsilV7WCjQvf3Wx4axXknDWCokPEsRfb0w9U73kIqZcvz8qfn5V1E0pbKjHpuL1XfHaD9oD8LoMf9vRjzOlHbJHneopBuUr0z61XvhOwd5QR/tMW66rC9wwfEZKmGk4fyyKbMQHMzy3YSt0/lIN3M0+fAOF+zMas0W+Pt2KdvFPT5MS4JzJgGPnXz3kBVB+cj+HuOarpW8YK+PBnUORHBoB9M5Kr7wFf6VsDTZwbyfvXJXFT1TjaFEyUZXNYVMujvSTWdnMVBf9dIEQZFt2KYnA8GgeHeLMo1x0sQHgothp3PpLCxL4fHXKU/mJc9bxai9NXqHal045U+K1RO5I7PVTFcDCr9jGk0XfDkw997AN9SBEC0p+/bO3x8F5SWGI4jULUdTMxWA5MZAeCyXeswOlPBUfdYma8F8wt7j89i27o8Th7Kh+wdO7HSzyvdVwNK370Q3fv4JK50Z6XzhcSv0w/13sn6n4ur8qbmaxidqaBcc1oK+imDGt7RLxbtvvIe+NbMeQD2J9wmyfMWheTVOyGlP1tBNmUglzZankzj9dJ3E7nZlOmNg/1kr7mZa+8kvaJ7Sj+yy6biYef8XjZzyqpZ8r1l7b0QaBj0WZ1xkH/DpdtweHIeN/7SnypfdYO2V97pBpOx2Qr+5Dv3o1yzveezP89160DI3gklcvki0ZtLNVT6DAccr3rHDCp9Dtqnbiiiryfd1N7hoD/cm8FQIVtn7/xs7xi+cNtjuHDboGfPcV6BLR5W+icr66LucCt4JtzFOBa60E9Y6UclMnl8ntK35QpjJ1Slr1TvNOqIOTFXxRf/8zF87tZ93t/CuQ0gPpELyLsxvtDNVCzp6SudQQfcC8YRRcWrczgeOT6LU9cXsak/F2hTXrGSe/reQiohpV9zHNz56DiE8KvDuCsn76fw3URR8fQ9pV+uec0Sk7ZgAKRNy/GgG+2dmwC8gYg+AeDVAH5NRB9qss33Yv62JCRO5EYo/fV9WWxfV2i5BzrbBmqgZc9PbauwaUAuRF5zkts7RgN7R43f3slVtQOLogPB5RIbXWv4gsBB5arT1+Mp2wfxNz/Z6y/GbfnVO4Af9G+6+zC+fMcB/PrIlKcqn3AV0EzZ8gKuWqdfCCVyuVzzjI29DT19xp+RW1+9o3ZF3L19CP0Jgv4JL9GawXBE2+Q/+/5vsGWgB198427vb+H+O4cmZOUGq2kA2D5cwGNjc5gs1TBYSHuzX0tVC5/48cMttwThfWE58dU7vEgPB/aaIzBdrsFyBIYK8kKlLqLSyN659eHjEEKqbc5TlZvYO+nQ0pWzFStQUSdEsB00W6NHlbtCPjaqloMD4yWcsr6ITW4VHCf6KzEXvSjyqr2jxIeaLbz+U2ztqZ8tlzbqaueLSskm201T8zXv+0nagoHhXGTX2TtCiGnIpOydAK4SQtwrhLiuyTZTUX9rf+iNUTPhYRylhj2s9I/PlDFSzGLnSKEtpV/MpgLJWT4o1AZqPEGrUZfNMGxZRJ3YapDji8x81cZsJdgKlni5xGYlm0Zw8Y2USfiDZ5+OJ6Yr+OqdBwDIoJ0xybtosb1zx75x+V0ovvlxJZgddhWwfL6rQjOpgNLni8RpG3q9xmClqoV8tt7eASKUvlEf9HeNFDBczCYL+nNV5N3+8OGqHCEEHhubw5WnjwSCm9d/Z0Zu+/iJeZw0GKzc2DGcx4lSDY+NzwWU/j2HJvHX//EIfvsLd3p3Oc0QQqBcs719JUT0scHBI23IuzLLdvy+OyGlL0s2g1aZyk8eHPUC5s0PPgEg2G2UiVT6im0Uvrvh+SyAEvRVpe8eGwfG52A7wg36siDirv0TsBpMTouiRwn6YXvn8GQJA/l04LzhzxZlL3IH0dmyhWPT897rct8lLtxICou6rqzTF0KcEELcKISInboatU2S5y0G2Qb2zqxyQEd5+iO9WWwZ6MFhdxJVUqbna17rBaYYofR5gpblxPfeCZM0kesrfcu1d/yT0aT4GblR78UXzLRJuHTXOly2ax0+d+s+lKqW58nz+C1bwLId/NxtLcCeN+AHccC3eFR7J58xUbX91ZaOz1SQNgk7R2R76en5mlT6MUqOq1ZKoTYM/NoAcNGOdQCQKOhPztcw4Aaf8Pq3o7MVlKq2V43DePaOW+VxZGrea1HN7BiWF/7RmQoGCxlvEXK+uO49Povfvv7ORG2Ga7YsvVVVciN7xzTc8lpHePYV3534JZsUu9i3ZTu49eFRPP+cTdg5UsB/PCj70kTZO0MxiVxAXkzCd99RSv+I4tdzkp9n4p66vtezTa79yh58/EcPt1Snr66oJs9BnoHu4MhkGVtC+40vEuEkLiDPo6LbiuGYcqHaOzqLYjYVeaFohB/0u0zprwT4y4vqYc5+/lAhg5lysG8GB/3w7WMS1GZrDB/QPNEKgDdBC0h+RfcSuWb9gWdEKNv5mo3ZctjTdydnJbV3asFOiW+8dBvGZqt46NiMd5fCJ3XNdvCrw1PKmsD+93Z8puKNg5O54TYMgG/PPDEt77aGCvK7nChVk3n6MYlcALhoxyAAeIvVN5qDMaUshMP2Dh8jfPu/NZSg40A35ir9sFcNwKsyAmSyM+u2PODv+U9fdg6OTZVx9fV3BgJIFJwvUZOg0UqfvJ8pQ3Zg5UoU39NX7J1ctKd/cKKEqfkaLt21DhfvWIf7D8ubdC+Rm/Hfu78n7VmOqZC9M1exvVJTJtre8T8/2zs8E3fnSAFnb+7Dl960GyO9WRydmm9J6edDSp+DedUSODJZf7HOK/ZOFLxC3tGpsved7hud9SYNtoIO+gugN5fG5v4cvvbzg3X+JP9/80AOliP8xlVukB8p5rwGZEdbmDwjm60Fg34xm0ZfLhXwdjcrt3xJV8dhNRJVoRFl73AiV71N5RW2Wi3Z5MDOt+E1W7j5CMMLKpYjcLtr7QDBCVBVy8FJgz3oSZteglO1d8LJw+PTFazvy2HAtQk4AMTZO2nTgGlQpL1z1qY+bFuXx+WnyMRcf08ajgje7YWZKtW8hOJwMYuK5U9046AfVvqZlIH+nrTn6Zcj7kxOHsp7d2WD+YzX3Iz711x+yjC+/JaLcHymgquvv6PhscfHrBowI0s2Dd+64Yl040qzNfk8P9CwXREO+hzce3MpFDJ+J875qg2DggUGpkFe4OdjSd3H9UrfP2f4Yqsmb+eVoL9loAf5jCxDfsYZG7ChL4uZstXajNxAItfx+unUbAeHT8zXKf1cA3sHkBbusel5lKq2N7dj3/E5z/JrBS+R2432TrdjGoRPvOZ8HBifw5/cdH/gMfbxeUFwTubyycBKHwgqjmZIhRg8MF70pE148+U7AnaK6vMlvaIbDat3/N9VpTZXra/esRynafUOvwUHlvCBaNmO1zfIt3cc3LFv3LM5whZKby6FkwZ7FE/ft3dUSwqQeZUNfVnPG2Z/NE7pAzJweYlc5Qs5Z0s/bn3nVZ7q4ovyVIM7uMn5KgZ65Hvz5+EKoAPjczAIdYEBQGCtXF40PThGE1vcuv3BfMZb3Y2/51zawIXbhvD3b74IY7NVXH39nd5nD1NWgrD3+lElmyl/jkbKkC0zuG30oHsnpU7OilvwRO1Xry5Fykslhu3CoXwm0EpALW0MW65hiyqs2PkCtPf4LE5RcmP8urNly11EJqnSV+wdW3jPmyhVMVOx6vZtvoG9w2Pgu5DT3QTwfM1uT+mbRuDnUrBqgz4AXLJzHd7xzFPxz3cfDkwu8pW+3Lns6/OiCZsGcl6iqCWlH6o5BuQsw//9rNMCf+MLCpDc3jEbevr1dsb4XBWOCFavGIbfMqFx0OckuKv0DbYI/AoQtmd4/HNVG7/cP4Gr3PrmcFAtZFPYMtiDxyelUrYUe6cYWkP1iekKNvTlvHrvw17Qj/dHc2k5CUsdbxSsJBv5+pOq0u8NlmIeGC/Jmb0R+0HOypUBlVeTCsN3CLKPvWyOx0GNlfqF2wbxlbdchAk38Eet+cBBuDdg70R4+oafGEybfiK3N5fyts+GLAVuUhd4P691sSxDthwBx103tidy/kQ6oFY9Cy9UvSM/Q/D5bPHwd8yTuvaNynLN4HPTmC7XULaiJ6dF4dk77gLuvJ8OuIUbYXuHHw9fxJliLu1VnF2wddD7+0LsHa30F8Dbn3EqLt4xhPd+537sG5VXYw7yHNinyxbuPngC7/uXX+OyXevwtFOGMVzMIm1SoF64Geqi6I3gCVoAEidyGwV9VWVl3EDM5X8BpU/k1SUnacPg5x2CFQU1y5F9g0xf6f/isXFULAfPPFNOsg4H1WI2qPTDXTYBGRDKNRtT8zWs78169s7hBEo/pyh9s0HQ54ASN0FLCIHJ+Rr6876nD/ilmAfG57AtZmr9SFHW9Hs95iPGywn9gXza25c8FlWpXrB1EF+95mKcKFXxms/f4U32YpIqfS+IGEYgkbtOSbaqDdcAtwtlKJHLAiCbNgLl0OWqjZ5M/fsOFTLBKipvQRs7QukHzxneR2yPlKo2Dp+YR8Vy6pR+b1bO5RAi3nMPk00Zcs3eiuvpu2Pb71p3bO0yjRK5gC9aNvfncMnOdd7ftaffIUyD8MmrL0AubeJ/fv1ulN0EJ+Bf0fcdn8Vbv7IHG/qy+PRrn4yUKetxN/TlAvXCjXAcgZmKVVe9EzemDe4Bkbhkk5LZO6Y7mSQy6CsbNqre4esQB/3wBcpyHLe1suGN/6cPj8E0CE89ZR1MgyLtnS0DsmRxtmJ5JZ/qGGcrljeRa31fzusCyX3J45QWEKxcSRL045R+ueagajmeveMvhejaOxOluiQus66YwehsJbKMkeGE/nAx6+1Lbt8RVqrnnTyAr11zMabna7j6+jsDgT8qkZtr0IYhZRDSnMidqwQqbHYMF3DtFTu9Fb6K2ZS33oT/foq9o5RDs70TZqiQQUYZj2GQbLdRsbyx5zMmiPy2CEx/T/CCW6ra2Dsqe+7U2Tu5lGfLJlX6ROTNArccf1IXH2dswTFNlb57/D7n7I3eHSLQXtBXm98tFas+6APSQ//4q56E3xydxv/7/m8CiVwAeN+//BqzFQtfeOPuQO315v6exEp/tmpBCCRS+oCcoAW0bu80q9M33AOaa+PDidyo3+PeK5zI9ZS+LbzZxKxIfrF/Audu6UdvLo2etInJUFAtZFJeH5rDJ+YD9o7q6XOd+oa+HIgIG/qyeGys5L1GHNmkQd89KeNWz+JSUz55hwoZEMn+O9PlGiZLtdgJN8PFbGACWlSQeOWFJ+GTV58v23K4Y56er3mriIV50kkD+PrvXILZioXXfP4OLzCx3dLXROmnDN8u8BK5s1VvYhYgv6/3vOBMrO+V50OkvWP59o5aDh0X9K+9Yhc++spzA3/LZ1IBT39Tfw5FpTcU06+UywKyw+ojT8i79Dqln0t5kwCTKn2A18m1XKXv2jvjJWRMA8OFYLD2PP3YoC/H+5yzNwQuiu0EfZ3IXUSeccYGXHP5Dvz9HQfw3XuPAJClk4BUEn/xqvNwxsa+wHM2DeQSe/rhvjvN4GRu0tu4xp6+8rshD9JmSr+hvcNBnxO5RvCWk5W+tHfktlXLwWW75K1tLm0GSjYBqchYQR2eLHn9+AF1tqbteaPcg3xDb86zVpolcplGlhkHyTilz+PmOv2UaWAwLxO0nAAdjqnK4L9zhVJUMCxmU3jp+VsAQFH6tYYq9Zwt/fj671yMUs3G1dffif1jc17FT2+Tkk3fIzbcRK7sL6NWk4WJ9vRt7z1Ue2e+Gl01c8r6omf1+Z/dxGzF9i4gLzx3E156/ua653LQ78+nkTJIKv3jsxgu+paf/5qNcxpxcDtvyxHeHdKhEyVsHsjVzbr1lH6MvXPeyf047+QBXLR9CETkCb+kffRVMmbwXFsK1kzQB4B3Pe8MPOmkftz7+BQKGRPDxQyGi1n8wbNPw/PP3VS3/ab+HjwxVUnUV59noIard+Lgss3FmJxFIQXfkzG9W964oJ9kERU+OVl1eDX5lpCTWkwKlJxetkvaAz0ZA1PzwYWk2dMHZFCsKvaOOluTJ3Jt6MsFfqqvFcVZm/0Ldnj9X5ViNhVpPzEc9PuV23Su1ee7F/UWXoX3Pd+tNLKjAH9fTpetpir17M39+Po1l6DsBv7fHJV2R9PJWZyEN2Qit2qLQIfNKHiykYqn9FVP351Y1uxzMoVsyrV35Gu9avfJ+NDLzq3bjoNmPmOixw3Oe0dnccr6+lxKscmdThz5jKz6EcLfTzVb1CVxAXief1zQf+n5W/Cdtz3VOxf63eNAe/pdQCZl4FO/fQGK2RR6c2lkUyZ+/p5n4h3PjF7LZVO/7Hd+NMEMSa/ZWmKl35q906hkMxzM8xkTPN+soMzIbdXe4WRhOqQ+aoqnz+PPmAYu3CYrF3Ip07vl5rLLYjaF4ULW9eilXaNOziK3Npxn43Llzvo+/8RpVL3z8gu2+ONv8J0SEfpyqdigzxcr9vQBWc8+Nlv1+uCH1SbDF1jOS8QFCSarJHKTqNSzNvfhH669BDXbwZ//UK6v20zp8z4zDdkn6cRc1e2700jp169nq9o7PEGwkb0T+bpuKWhc8zKGlX4hk0Ihk0KpamHvE/XlmkDQ3kpapw/IY47PWfV5kUHfvZgkvbj19aRhkD8PohW6ug3DSmXbugK+8MbdeOdzTwfQ2P+9dNc6ZEwDf/RP9zVcLxaoX0ClGZ7ST2rvEMGg6O0DiVyiQAldMeDpRz+n7r1CbRhYLaodNS07WL1zwdYBv8pBOTk4uBRzKRgG4aSBHq+nkb/AB7kLmNs4Pl3G+t6cd/eiKv1GJ92FSqlcsw6FshVD9OQsz95RlX6vrMqZaqL0WXXz6mtJlf5M2UqsUs/YKAM/2zPqnWXk5CwliKQM8u6k1jW1d2Kqd1JBpd9S0M+aAU8/rn1wv6f0U8hnTOwfL2GmYnkrmamox3fSGbnytU1vvo46/qj5F/kmSj9q/EOFbMPYEkdYYC0Fay7oAzKYv/LCk5pud9qGXnzwpWfjtkfGPGUVh7c+bkKlf86Wfmzuz2HHuujyvzCmQbEniarayQgu3hzushn1e9R7Af6J7lXvKEsxWk6wTp+tHSConDhAcrJ2y2CP171UVTOsLp+YKQfU/YaA0o8/6QyD8Kwz18txJgr6cYnciKBfzGBspoITc3wXEL2P2V/mfEqzIJFpUekzp23oxTfeeine+dzTA62bI1fO8vadbNvLCf5GKrQ3m0LVdgKllX6dvurp25iv+jNamyHtHTtw1xCFp/Sz0t657/FJAPVJXCAY9FtR+j1pJegrJadRQZ8/X6PjT+WSneu8+Sqtoi5os1S01g1oDXL1RVtx3+EpfO7WfThnSx9e9KT6xBOgKv1kX+nJQ3nc/u5nJh6HaVBsGVfY0+eD06Bg4AlcHBIsolJx18GlULlozRYyEWsQtg8X8PpLtuJVu/2LqHrysdLn9hAnDfZ4nThVNVPIpjBbtfDEdAWnKH2KNrgVJRmlUiiOz79hN8ZmK01PmKie+hXLxl//xyN4dHQOGbcdATNczGKuans2X39c0M+xp59M6XPQm6k09/TD7Bop4m1XnRKYsdtojdyUYQQu9I26P6otEzIpuf8qloNMygCRfxxWaq6n36a9k0TpFzIplGvyODxnS3/dts3srThUe6enib2Tb1KyGeZ/PH1X4nGE4e8ks4T2jg76CXj/i8/GQ8dm8M5v3odT1hfrqnwA39NXlcdiIpV+9EFXb+/I7QqZYDlc4kSu0q5XTTRzIlf1+tOmUZeM61ECmOfp5zjo570JYoGgn5FJvuPTZTx1lz/BZb1r7+SzzU84051b0Yy+nrQ3SYz5ix89jOt/+igAmYBTvzeu1d93fBa9odbZKrzvWenn042PBTVItRKwVDhIEEX7wCm1Tt/93SA0XNGpoMyb4BLmiuUvR8jvWbFbtXdS7oxc2RI67m6Tk+j5jOmp7KedOhJ5sW2WyI4jrySr1eeFa/QBtZ9+8tdvF1290yVkUgY++7onozeXwrVf3uMl9FSm5626XvqLiWlQbGAIKnilYiY0UcwI2DsN3kt5PbVemG0Tv89+9ItwEEgZ5CXauEJHvX0O2zvjc1VMly0v0AO+vRPXVrkdwvbO7XvH8IXbHvXuSsL2zXCv/Pve47OBqp4w4aCfi5ipqqIq3XYDirccYqq+/w3gBxEu2QSAre7SnXGofXIYuUhLsG3DXCVY596MQtZEqWbHLvjC7BwuYPu6PM7c1IdHnpBVSi+MqK5Txwq0GPTTfrGD+rxNEXdAW4fyyKQM7BhOZsUuhMwy2Ds66CdkfV8On339hTg6NY93/OM9dYld2Xdn6W6cTGrk6SvbGX4itxC66zCT2jvKWp2q4iAir25aPhb9GhwEsim/VS/nOk4aVIO+/9rFbMpbiF6tb5Y9yc3EgSUJHPSFEJgsVfEHN96LHcMF/OO1l4CoPlHL9fcHJ0qxSVxAfvf5jImJudaqd8K/twI/Ly4RrC6/xxfwXSP13rgKf8ZxZfGYitK6mI/Dqfn66pdGFLIpCCGT5Y3WgF1XzOKWd16F0zf2elbQs87aELltoGSzRXuHkbaV3M9Rn+XkoTwe/tDzA6tpLRW8v5bS3tFBvwUu3DaID7zkHPz04VH8xY+Cid2kfXfapZhLxb5+nKdfF/SVvd2oll19PJwUTZuG12YgLmHKijCTMvDyC07CX73mfE8hb4kJ+gVl6r9q0RAR1vdm6z7LQujvScNyBEpVG+/59q8wNlvBJ19zAU7b0Is3XLINV5waTMJx0HdEcFWoKIrZFFgPJE3kAgtX+nEBT51jwXbKroiEqAorWnXluIplexeWbCjoJ7V3+MI/5q5DnYSvXnMxPvf6J8fmUdKm4eVDWkrkKhVuaTfJvWWguTW41CyH0teefou89uKt+NXhKXzmln04Z0s/XuDedkZ12FxM/vgFZwZ6jKsE6+/VCVFm/HZNjinTILepWnDDlEl+0I+zdzylL5cbfJlSQ7++N4e0SYHWykCwBl+t3gGAU9b3NrSjWoUDyJf+8zF8/1fH8IfPOwPnniSThB986Tl126vljXHBhynmZAuMjLJ+cBxqYr5dpc/eeFzAUz1iLtfcNdLYptjQm0NP2gwFfSdwMQf8TqpRDdei4CKHsdlKQ6WvcuamPpy5qT6HplLMplGuJb+QAEGlbxqydUJUEne5WY7JWTrot8H7X3IWHjo2jf/7zXuxa6SI0zf2YnrequvOt5isb5CgZMFNJJUxK69wUjlpIlfdNpxsS5sGys3sHff9oywH0yBs6u/BwYlSyN7xT0Ku2GH+8jXnNbSjWoUD9yf+/WFcvGMI116xs+H22ZSJvlwK02Wrob0D+FVKSapx1H5BC0kSps34fE9KsXd4se6dTewdwyBsW5ePCPpsPSxc6S9mwUNfLoWx2UrLk7OYlEE4Y2Mvdm8fWrQxtYu/MLq2d7qKbMrEZ19/IQrZFK79yl2YKtWWXOk3ghO0HMjzcZ5+K0GffFtARfX041pI5EI2QBj29cP2DiADSjiw9ubSixwk5OsXsyl84jXnJ5pEw3311Zm6UbDHnGRt1MVQ+vw6cQGPg4dp+G3Cm3n6gFySMBD0a/XVO616+mxPjs1WW5qX0IxiLhXbsC4Odf+YBuFbv3sZ3nL5jkUbU7voNgxdzIa+HD73+ifjyOQ83vGPd2OqtLSefiM4gPMxr/a7idou/Hvka3o9W4KHSNo0UKoFu2+GYeUXdwvvB33/+TzW9X3ZRVX1UWwfzqMnbeIjr3hS5GScKNjXb6b0+XMkSTyrn38hSj+TMmIvGuoMzzddth0AGrZgYHYMF3BoooSau7RhxXK8O5Nw0E+6+DcXOtiOSGzvJEFdECYpYaXfLXTljFwiuoGIbiei61rZjoj6iejfiOjHRPRtImp+5HU5F24bwvtfcjZufXg0cS/9pYCPWQ7kPTFBP2mXTUBpvWCG7R3y7J24yWIcwOJOxC0Defe1/edz4GinM2GrnDSYx/0feC5e+KToMsAouFa/qaefDS5B2Agi35ZZqNKP+67VVr3vf8nZePTPXpDoNXcMF2E5wusYuij2jvLdLWbQL2ZTLU9uUy/KS5k0bZWuWzmLiF4BwBRCXAZgMxFFdiqL2e51AD4hhHg2gGMAnrewoXcHr71oK65+yskAkvfdWWx8pc/2TnT1jipomqlpwwv64USugVLNinyMUUs2o2ClnwnYO/I5SSZXLQat9kXhXjfNqnd6PXsnWSDkk3xBnn7KiA166iIqAOraBsfhV/DIPvbq5CyeleuvG5AsjPS2WV7ZjJMG8y0fN+raDN2k9DOhC+tS0Ko0vRLAje7vNwO4HMAjSbYTQnxGeXwEwPGoNyCiawFcCwBbt25tcXjLDxHhAy89GyO9WTznrI0dGoP8yYEsib2TtGQzHTohUgZhshpsuRwml2oc9J915gb87pW7cMYmv+6Zx7pcQb9VWrZ3EgbxbMrADBYWBLcM9MTaVCO9WWzoyyby8VU46D86OodnnMF1+sHa9lY9/WzKRC5toFxrPDmrVd753NO93kBJ6QlU73RP0N81UsDJQz3YvoQTwRoGfSL6PIDTlT89HcAN7u/TAE6JeWoBwOGo7YjoUgCDQog7o54ohLgewPUAsHv37uaN7LuAbMrE/3nO6c03XCI4mHMc39AnF3Y/Y2NwMkkgkZugZBOoD+yZlOG3YYh5EbVkM4r+fBp/+LwzAn/ju5J2epAvBzyuuLbKTMGr3kkeCIFgJU+rfOlNT4kNXL25NH7+nme1/JqD+TT6e9JeMld6+v7+zqQMTLgN6JJe4ACZRJfllYuXyM2lzZbvlLrV0z9pMI/b3vWMJX2PhkFfCPFW9f9E9EkALCmKiLeHZqO2I6IhAJ8C8Mo2x6uJIGzv9ObSuCOimZt6a9/M3uEgUlenbxBKVbZ3YpR+k0RuFF4it0uD/guetAlV22la417sgL2zFD1hiAg7hgtK0LcD6ly1H1qZLd3Xk5bzGBZR6bdDvkuV/nLQ6je/B9LSAYDzAOxPup2buL0RwLuFEAdafF9NA4yQvROH2Yq9E+qhz6RMw5txGldL3KxkM4ozNvbi967chWfHTLfvNH25NN546famF8veFu0dDp6LaXcsFjsDQb/e3mGiFmSPg4sdltKzTkIgkZtw9brVQquf9iYAbyCiTwB4NYDvEdFZRPShZtsBeAuACwH8MRHdQkSvWdDINR4UKtmMo5U6fd40nKxNBxqwxdg7DSZnxZEyDbzreWc0tU+6nVZKNgH/O1qODo6tsmO4gKNTZZTchU8CSl+pOkqaHAb8YodWjo2lIGMasRVqq52WErlCiGkiuhLAswF8TAgxBWAKwHUJtvus+0+zyPgzcpsF8uSefspbDL1+Rm7U7yrNPP3VDNs7SYN+Nyv9Ha6V9fATsoIn4Om74261ER5PjOu00ie3BflM2eoqT385aLmwXAhxAn5lzoK30ywcr2VCQssGSD45K6zm1f83a8PQad+2E7RavbMYnv5Ssd1d1e3Bo9MAgjYOj7uVJC7g99/phmODl0zUnr5mxRGekRtHoMtmM//f3TZqchYTV6fvT85ae4cX16K3UrKp/uwmuGzzwWOyp324egdoI+i7Sr8b7gJ5QuBa8/R1w7VVAIv2Zt5quBtnI+JaK6uBPu62OJc2cd0Lz8RVZ6xv/CarkOFiFn25VOIFN7pZ6ReyKWzoy+I3rtJXA3W2zXGzp98tSh8ATO3pa1Ya4ZLNOMw2SjYbJXIb9Qe55mmNO1euVgrZFO5933MS9w/KNJnI1ml2DBfwm6Ou0o8o2WzX0++Gz8tBf615+p3/5jULJqm909KMXK/hWsjeiVgzVxOklYZx7Srm5WLHcNGbeRtVvbOSPf0ez95ZW8dx5795zYIx2rJ3km1b33tHUfprzAtdCjJd7OkDwI5hfwF1ddZwu7aUV73TBZ+X111ea57+2vq0qxRqw95pWrKpLLOnolo6WukvnG4u2QSk0mcWxd7p6T57Z615+p3/5jULxm+t3Hg7VbQnVfr1/fSTefqaZHBb4G5q76uiJqQDC7m7lTz5FpX+qeuLeOYZ6/HkrYOLM8AFkM+uTU9fJ3JXAeGVs2K3Uz39piWb0Uo/FZictbZOlqXgjZdtw2WnrOv0MGLZOpSHQXJR+EAbBlP+3qrSL2RTuOFNT1nUMbYLl2yutTp9HfRXAe1U7yQt2QyreU7smgYt+QpXa4H1vTms7+3OdtKA9N5PHsrjwHgpsk6/WxPQSdjUn8NAPq2VvmblkbThmnpRSFyyGVOnv9ZOlLXM9nUFGfQXoXqnm3j9Jdvw4vM2rznx0p1GoqYlks/IbaPLZl2dvhH4qVn9sK8fNTkr6apZ3UjaNLzFcdYSK3ePaTz8RVQWv/dOfcO1tdmZcC1zyc4hjPRmA8sdetU7K1jpr1W0vbMKCC+XGEdrXTbjGq5F/12zenneOZvwvHOCi8ivBk9/raLP3FVAO/ZO06QvRSt6tnsyWumvaTxPv8XqHU3n0UF/FcCiu6m900LJphGTyOXb+m6tK9csD9reWbnoM3cVwKq9WXJWdWSaFSyYTdowaE9/bbMaqnfWKjrorwL83jvNtmvB3jGjG67xRUD33VnbeI3itL2z4tBn7iqgnd47TUs2Y5R+OmamrmZtcemudfi9K3fhnM39nR6KpkV09c4qwEwY9FtS+jHBPa09fQ2A3lwa73reGZ0ehqYN9Jm7CmireqfJnvf76Ud7+mHbR6PRrAxaDvpEdAMR3U5E17WzHRFtIKK7W31fTTzJ6/TV3xeq9HXQ12hWIi0FfSJ6BQBTCHEZgM1EdGob230cQE+7A9bUw+WVzUo2iajlPj3hGbkpb6auvknUaFYirZ65VwK40f39ZgCXt7IdET0DwByAYy2+r6YBSfvpA36wb1qy6R4Z4Zm36ZTuvaPRrGQaJnKJ6PMATlf+9HQAN7i/TwM4JeapBQCH1e2IKAPgTwC8DMBNDd7zWgDXAsDWrVsbDl4j8er0E0R9ua1IYO9E2zjs8esumxrNyqRh0BdCvFX9PxF9Er41U0T8ncJsxHZ/BODTQojJRjaEEOJ6ANcDwO7du0WT8Wvgq/YkLWL5wpC0ZDOs6L1Erlb6Gs2KpNUzdw98S+c8APtb2O5ZAN5GRLcAOJ+Ivtjie2tiSDojV90mub2ju2xqNKuJVuv0bwJwGxFtBvB8AJcQ0VkAXiuEuK7RdkKIr/ODRHSLEOKaBY1c45G0ZBOQSV+DkrRhjvbuveodPSNXo1mRtHTmCiGmIZO0dwK4SggxJYR4IBTwI7cLPX7lAsasCeEncpPZO8m287dX8dowaKWv0axIWp6RK4Q4Ab8yZ8HbaRaOtzB6okRu0u1i6vR1GwaNZkWj79FXAS3ZO0qtfiM29udQzKZQzAZ1QUovl6jRrGh0751VwFLYOy84ZxOuOG0E+UzwEEnr6h2NZkWjz9xVgKf0E9o2Sap8DIPQl0vX/d1P5Gp7R6NZieigvwogT+k339Y0qGm5ZiN0GwaNZmWj7Z1VgO/pJ7N3kszcjSNlGvjAS87G004dbvs1NBpN59BBfxXQStA3KNl2jfhvl21f0PM1Gk3n0Pfoq4CWE7naj9do1iw66K8CyGu41nzbpCWbGo1mdaKD/ioh+UzbZNtpNJrViQ76q4Qk/XQAHfQ1mrWOTuSuEv7kRWfh4p3rmm5nEDVdH1ej0axedNBfJbzh0u2JtjMoWQtmjUazOtGab42h7R2NZm2jg/4aw6CFzcjVaDQrGx301xgLnZGr0WhWNjrorzG0vaPRrG100F9jyMlZOuhrNGsVXb2zxhjMp1G1nE4PQ6PRdAgd9NcY73/J2ajZotPD0Gg0HUIH/TXGQD7T6SFoNJoOoj19jUajWUO0HPSJ6AYiup2IrmtnOyL6DBG9uNX31Wg0Gs3CaSnoE9ErAJhCiMsAbCaiU1vZjoieBmCjEOK7Cxy3RqPRaNqgVaV/JYAb3d9vBnB50u2IKA3gCwD2E9FL496AiK4loruI6K7R0dEWh6fRaDSaRjQM+kT0eSK6hf8BeDuAw+7D0wA2xDy1ELHdGwE8AOBjAC4iordHPVEIcb0QYrcQYvfIyEhLH0aj0Wg0jWlYvSOEeKv6fyL6JIAe979FxF80ZiO2uwDA9UKIY0T0VQAfBvCpNset0Wg0mjZotWRzD6SlcyeA8wA81MJ2JQA73cd3AzjQ9M327BkjoqbbLRPDAMY6PYgY9NjaR4+vNbptPCp6bD7b4h4gIZJP1CGiPgC3AfgPAM8HcAmALQBeK4S4rsl2DoAvQVo9aQC/JYQ4jBUCEd0lhNjd6XFEocfWPnp8rdFt41HRY0tGS0pfCDFNRFcCeDaAjwkhpgBMAbguwXYA8KoFjlej0Wg0C6DlGblCiBPwK3MWvJ1Go9Folg89Izc513d6AA3QY2sfPb7W6LbxqOixJaAlT1+j0Wg0Kxut9DUajWYNoYO+RqPRrCF00NdoNJo1hA76IYjkWoL8sxvpxrERUb/7s+vGxnTr2IgodiLNckJEaSI6iYgu6vRYGkFEF7g/zU6PJQwRXe7OU+padNB3IaLLiOhrAP4nEW0QXZbhJqJeInonERndNDYiehoR/QOATxLRmUII0U3B1Q1knyGiVDd9bwBARBcR0dcBfIGIXtUFweJFAK4FcD0RvYaIch0eTx1ENAzge0R0shDCJqKuiGFE9HQi+mcAbwLQ1cKxK76wLuFaALdCTpX+IyIa6vB4wpwJ4KWQJ2ZXHFBElAXwuwBuAHA3gL8CgC4LrgUAL4Tcv+iWIOHyZgDfAfABAM8CkO/scPA6AD8E8A4Alwghyh0eTxTbIWf3/w8AEEJ0y4LP7wZwsxDiGgBnAF13Hnh00wmw7BDRTvfnCIAqgH8WQvwDgHEAz2XLogvGlwawA8BPALy803ciPC4ApwE4JoT4dyHEJwHUusGqcFUzT3nfBuDLAH7LVYdOJy+Y7tieQkR5yK6z/yKE+Bnk/t3eofHsdi+GNwD4OYAagOcQ0VOJqKfxKyzL+C5U/nQcwE0AnkJET3e36YjN447tYve/fwPgeUR0G4D3E9HvE9EZ7nYdF2gqazboE9GzIXcUhBCjkD2BXuI+/G+QTeE2dmZ0deOrAbhNCPFeAL+EVIidHNen3XH9CsAH3b+fDmBSCNGxBnlEVCSib0Cu5/A7RPR8d0zvBfAtSDXWEQUWGts1AC4D8G0hxLwbcPcDONih8fwO5J3GPUIIC0AOwL9Dng8v7kTQCo3vWiJ6PhENADgdslnj7wF4uxt0l3V8obG92T0nCgB+A+CVkG3kswAuBbpP8a/JoO8qgysBbFT6+n8YwFtcz3wPAAHZLG7Zr9Tu+K5yx/d77p+fcH9+F8AOIjpvucemfG8blHFxX6VJAJa73clE1HKLj0VgHvLW/90APgrZDPAZACCE+AyAk4noqe4Yl/vYD49tJ+R3yRbFJiHEESLaTkTbOzCe7QCe647nJ0KI/wXgHgCb3TxNp7+vrQCeCWAf5BodBoAnQbZnd5b5eFPH9ucANkGKxo8KIY67IjID4GRAK/2O4wZ1G8BXADwH0i7ZKIT4BYD/AvBeItoAeRt5LrC8V2plfF+GPAl/i4hG3KQVCSEOQXYvfcdyji3ie+Nx8ftfAGCQiN4L4LMABpdjXCFGIC+OuyCV868BbONqDwCfB/BnQEe8YB7bKe7YfgVgJxGd6wb5HBF9DPK7K3RoPDtcq+d5RPRMSKU6CHTF93UfpMp/K4CrIYPtlwAcFEI47h1KJ8b2KIBHIL+n04nouW782AHgBKCVfsdgpaIcvONCiDHIxNV73L9dB2AU0go4FW5isoPjG1XHpxw8P4D0NTs1rvD3BsgT4MmQuZGXu2Nf8rGpfq4Q4hjkrf7ZAEwAD0Oqwu3u4/8K4G/IpUNjO0sZ2yxkzmEjpGqdBPBiIcSvOzCehyBzWadBJpSvgQxaH1zssbQ5vkcATEBaKN8E8GohxEewDJ17E+7LcciY8VQA/wBgBsBnlnps7bBqgz4Rnav8bnDQcr3B6+Eu6CKE+CiAXUT0VCHElGsDvBRSSd/fJeM7jYgudR8nd5xLsrh8G+N6qrv5PQAuE0J81M1BLAlE9Cwieg8RrXPHYbt/fyERfQZSFV4C4KlCiHFIW2ALP18I8U3h0iVj2wR5R3KeEOLPFlOxtjieCcgL95AQ4p8B/DchxPv4OUtBi+Mbg7yY54UQnxFCVIjIXCoV3ca+PB1ATgjxJwBeJIR4x1J+dwthVTVccwOicH+/D8C1Qog73f+fDuCdkCvYfFII8RPenoheC2C3EOIPunh8Fwoh/k8XjuspQojfX4pxhcdHRB+HvOgcglTJnwDQB+C9AIYA/LUQ4maSCdzLIO25CQDfFEL8WxeO7QSAGxdzbIvwXX1LCPH9xRrPShrfIuzLby7ld7doCCFWxT/IbPmA+/tFkF7b15THPwUZzNTn0FofX7eOKzS+Iff3PwSwzv3btyFV8v8F8Jbw+CB98TdCJkvNtTC2bhvPShpfN49tsf+tCqVPRG8G8NuQJW9fgqx/nocsefyZEOJviSgjhKi623u2xVoeX7eOKzS+10JeiL7l/vk/hExq/z2Azwkh7lC2N4Wf8F7SA7vbxtZt41lJ4+vmsS0FK97TJzmx6sWQdbv3AbgCQEXI2YR/CeB1RJQVQlTJLeta5sDVlePr1nFFjO93IS9GFwghfuSebAUAO/lEJDkzGML1UJchSHTV2LptPCtpfN08tqViRQZ9ItpKRFcT0UYAJ0H6oo9AtlF4vhCiBABCVkHcDn8N32VJrHTr+Lp1XAnGdwtkKwWuJrIA3E5EpxPRhyBtqTU1tm4bz0oaXzePbTlYcUGfiM6BLNk6G8DbAfQJ2ToBkCVvvwk95eMALiaigWVSNV05vm4dV6vjc+82ToNMLn8ewHEhxG1raWzdNp6VNL5uHtty0YlZk21BRK+ErCD5KYCHhBDvJTmBZDcRWUL2L9kCmW0HyRmrh4QQE0T0YiFEZS2Or1vHtYDxPQlypuN7AXxWyFLDNTG2bhvPShpfN49tuen6oO/6yTdAZspLkKVUx4hoEMCdkCVUVxDRnZCzQtcR0T8COArg/QCwlIGrW8fXreNa4Pi+Adl35SNCiO+tlbF123hW0vi6eWydYiXYOw6AfUKIN0LODrwc0oc7RQgxB+BByBmEWyD7X1wG4OtCiN8XQkzFvOZaGF+3jmsh4/uaEOJdy6C6um1s3TaelTS+bh5bR+j6kk33Sv1UIcSt7v+/ANnkaw6yD8wBAD+GrJVNCyH26fF177hWwvi6bWzdNp6VNL5uHlun6Hp7R8hp6bzDTgawQwjxLCK6GsCfQs6EOwqgLIRYtta03T6+bh3XShhft42t28azksbXzWPrFF0f9Bm3hKoG4GdEdCZkr4sfQu60Q0KI43p8K2dcK2F83Ta2bhvPShpfN49t2RFdMC046T/IRR0cyC6Tb+z0eFbK+Lp1XCthfN02tm4bz0oaXzePbTn/db2nr0JEVwG4GMAnhNsaoJvo1vF167iYbh5ft42t28YTppvH181jW05WWtDv6l4X3Tq+bh0X083j67axddt4wnTz+Lp5bMvJigr6Go1Go1kYK6FOX6PRaDSLhA76Go1Gs4bQQV+j0WjWEDroa1YtRHQ+EZ2/gOe/n4iuXIrnLnRsGk276KCvWc2c7/7rRs5H945Ns4pZMTNyNZpWIKL/B+Dl7u9vEEI8k4huAfBLAE8SQjyXiIoAbgSQA3BACPHf3e6L3wRgQnZmvIWI8gC+DGA9gF8JId4W855Rz416j6ixJXoPjWahaKWvWZUIId4N4COQ7XGf6f75EgB3CCGe6/5/E4BPA3g+gO1EtAHAtQD+VQhxFeS0fbh/u18IcQWATW6v9Siinlv3HjFjS/oeGs2C0EFfs5a4Xwjxz8r/awCuAfA1yL7qPQB2QK4ZDAB3uT9PB/By905hJ2Qb3iiinhv1HlEkfQ+NZkHooK9ZzcxD9koHERHcVZEU3gLgWwB+G7LVLiBb7Z7l/n6++/MhAH8lhLgSct3guG6MUc+Neo+osSV9D41mQegZuZpVCxENQfrpPQDeDeCDblDlx68A8BnITosm5HqoD0H68gQgDeCPIfMAfwtgI4BpAK8VQkxHvN9wxHOd8HsIIX4WMbY9Sd5Do1koOuhrNBrNGkJX72g0beB67ypTQoiXdmIsGk0raKWv0Wg0awidyNVoNJo1hA76Go1Gs4bQQV+j0WjWEDroazQazRpCB32NRqNZQ/x/c7nxpZwTkkgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xret.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "9182c855",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='trade_date'>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 累积收益率\n",
    "((1+xret).cumprod()).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "1cfbb4d7",
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'statsmodels'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "Input \u001b[1;32mIn [45]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mstatsmodels\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtsa\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m statstools\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'statsmodels'"
     ]
    }
   ],
   "source": [
    "from statsmodels.tsa import statstools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "499ef3d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "statstools.acf() # 系相关系数\n",
    "\n",
    "statstools.pacf() # 偏自相关系数\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "trader",
   "language": "python",
   "name": "trader"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
